Buch, Englisch, 338 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 699 g
Reihe: Trends in Mathematics
Buch, Englisch, 338 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 699 g
Reihe: Trends in Mathematics
ISBN: 978-3-319-70823-2
Verlag: Springer International Publishing
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Mathematische Analysis Differentialrechnungen und -gleichungen
- Mathematik | Informatik Mathematik Mathematische Analysis Funktionalanalysis
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
Weitere Infos & Material
Posterior contraction in Bayesian inverse problems under Gaussian priors.- Convex regularization of discrete-valued inverse problems.- Algebraic reconstruction of source and attenuation in SPECT using first scattering measurements.- On l1-regularization under continuity of the forward operator in weaker topologies.- On self-regularization of ill-posed problems in Banach spaces by projection methods.- Monotonicity-based regularization for phantom experiment data in electrical impedance tomography.- An SVD in Spherical Surface Wave Tomography.- Numerical Studies of Recovery Chances for a Simplified EIT Problem.- Bayesian updating in the determination of forces in Euler-Bernoulli beams.- On nonstationary iterated Tikhonov methods for ill posed equation in Banach spaces.- The product midpoint rule for Abel-type integral equations of the first kind with perturbed data.- Heuristic parameter choice in Tikhonov method form minimizers of the quasi-optimality function.- Modification of Iterative Tikhonov Regularization Motivated by a Problem of Identification of Laser Beam Quality Parameters.- Tomographic terahertz imaging using sequential subspace optimization.- Adaptivity and Oracle Inequalities in Linear Statistical Inverse Problems: a (numerical) survey.- Relaxing Alternating Direction Method of Multipliers (ADMM) algorithm for linear inverse problems.